ML inference in PHP by example: leverage ONNX and Transformers on Symfony

Machine learning and AI have expanded beyond Python and Node.js, allowing PHP developers to run AI models directly within their applications using tools like ONNX and Transformers. This guide from a Symfony conference presentation by Guillaume Moigneu illustrates how to implement machine learning inference in PHP, leveraging the Transformers PHP library and the FFI extension to enhance performance. Transformers, a neural network architecture, enable efficient parallel processing and better context understanding, thus revolutionizing AI tasks such as text classification, image classification, and text generation. The article discusses practical use cases like sentiment analysis of reviews and automatic image tagging, while also highlighting the importance of ONNX for cross-framework model compatibility and the Hugging Face hub for accessing a wide array of models. For deploying these models, options range from running them on small PHP servers to using external APIs or self-hosted solutions for more control. Although there are limitations, such as the lack of GPU support, the potential for PHP developers to integrate machine learning into their applications is significant, with recommendations to start small and expand gradually as the ecosystem evolves.